stactools

stactools is a command line tool and library for working with STAC. It is based on PySTAC.

This is the core stactools repository, which provides a basic command line interface (CLI) and API for working with STAC catalogs. There are a suite of packages available in other repositories for working with a variety of datasets and for doing more complicated oprations on STAC data. See packages for more information.

Installation

To install the latest stable version:

> pip install stactools

From source repository:

> git clone https://github.com/stac-utils/stactools.git
> cd stactools
> pip install .

NOTE: In order to read and write Cloud Optimized Geotiffs, GDAL version 3.1 or greater is required. If your system GDAL is older than version 3.1, consider using Docker or Conda to get a modern GDAL.

Optional dependencies

stactools includes some optional dependencies:

• s3: Enables s3 hrefs via fsspec and s3fs

To install a single optional dependency:

> pip install stactools[s3]

To install all optional dependencies:

> pip install stactools[all]

Docker

To download the Docker image from the registry:

> docker pull ghcr.io/stac-utils/stactools:latest

Running

> stac --help

Docker

> docker run --rm ghcr.io/stac-utils/stactools:latest --help

Documentation

See the documentation page for the latest docs.

Packages

stactools is comprised of many other sub-packages that provide library and CLI functionality. Officially supported packages are hosted in the Github stactools-packages organization, and other subpackages may be available from other sources.

There are over 25 packages that translate specific types of data into STAC, including imagery sources like aster, landsat, modis, naip, planet, sentinel1, sentinel1-grd, sentinel2, sentinel3, landuse/landcover data (corine, cgls_lc100, aafc-landuse), DEM's (cop-dem, alos-dem), population data (gpw, worldpop), pointclouds and many more.

There are also cool tools like stactools-browse which makes it super easy to deploy a STAC Browser from the command-line for any local data.

For the list of officially supported packages see the list of STAC packages on the stactools-packages github organization. Each package can be installed via pip install stactools-{package}, e.g. pip install stactools-landsat. Third-party packages can be installed in the same way, or, if they are not on PyPI, directly from the source repository, e.g. pip install /path/to/my/code/stactools-greatdata.

Developing

Some packages require environments with more complex environments than can be set up just through pip. For example, the stactools.aster package uses rasterio functionality that required a GDAL enabled with the HDF4 format. Because of this, it's recommended to utilize docker or conda to ensure a consistent environment.

Using docker

Build the container with:

> docker/build

Once the container is built, you can run the scripts/ scripts inside a docker console by running:

> docker/console

A complete build and test can be run with:

> docker/cibuild

It is recommended to do a Docker CI build before submitting a pull request to ensure your changes will (likely) pass Github's CI.

In scenarios where you want to run scripts in docker/ but don't want to run the build, images can be downloaded via the pull script:

> docker/pull

Run a Juypter notebook:

> docker/notebook

Using conda

conda is a useful tool for managing dependencies, both binary and Python-based. If you have conda installed, you can create a new environment for stactools development by running the following command from the top-level directory in this repo:

> conda env create -f environment.yml

Then activate the stactools environment:

> conda activate stactools

Finally, install stactools in editable mode and all development requirements:

> pip install -e .
> pip install -r requirements-dev.txt

Using virtualenv

If not using docker or conda, it's recommended to use virtualenv to keep isolate the python environment used to develop stactools. See virtualenv documentation for more detailed information, but as a shortcut here's some quick steps:

• Make sure virtualenv is installed
• Run virtualenv venv
• Activate the virtualenv with source venv/bin/activate

Running the CLI against development code

You can run the CLI through docker by running

> docker/stac --help

or in the local environment with

> scripts/stac --help

Unit Tests

Unit tests are in the tests folder. To run unit tests, use pytest:

> pytest tests

To run linters, code formatters, and test suites all together, use test:

> ./docker/test

or

> ./scripts/test

The test script also runs lint and code quality checks.

Run a Juypter notebook:

> scripts/notebook

Documentation

To build and serve the docs, all of the requirements must be installed with scripts/update. Make sure Pandoc is installed. Also make sure sphinx is available, which should be installed with requirements-dev.txt. You can also run the following in the docker container using

> docker/console

To build the docs, you can use make html, and to build the docs and start a server that watches for changes, use make livehtml:

> cd docs
> make html
> make livehtml

If using make livehtml, once the server starts, navigate to http://localhost:8000 to see the docs.

Use 'make' without arguments to see a list of available commands.

Adding a new package

To create a new stactools package, use the stactools package template. stactools utilizes Python's namespace packages to provide a suite of tools all under the stactools namespace. If you would like your package to be considered for inclusion as a core stactools package, please open an issue on this repository with a link to your package repository.

Releasing

See RELEASING.md for the steps to create a new release.